Questions tagged [crossover-operators]
For questions related to the crossover (aka recombination) operator in the context of evolutionary algorithms.
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How do mutation and crossover work with real-valued chromosomes?
How exactly are "mutation" and "cross-over" applied in the context of a genetic algorithm based on real numbers (as opposed to just bits)? I think I understood how those two phases are applied in a "...
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Why is cross-over a part of genetic algorithms?
Genetic Algorithms has come to my attention recently when trying to correct/improve computer opponents for turn-based strategy computer games.
I implemented a simple Genetic Algorithm that didn't use ...
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Is elitism preferred over non-elitism in the cross-over operator?
There are two potential approaches when performing cross-over operation in genetic algorithms.
Use only the elites in the pool, probably the ones that are also going to be directly transferred to the ...
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In NEAT, is it a good idea to give the same ID to node genes created from the same connection gene?
Do I have to prevent nodes created from the same connection gene to have different IDs/innovation number? In this example, the node 6 is created from the connection going from node 3 to node 4:
In ...
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What is the difference between "mutation" and "crossover"?
In the context of evolutionary computation, in particular genetic algorithms, there are two stochastic operations "mutation" and "crossover". What are the differences between them?
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Is there an efficient way to implement a random crossover of individuals stored in a matrix?
I am using a GA to optimise an ANN in Matlab. This ANN is pretty basic (input, hidden, output) but the input size is quite large (10,000) and the output size is 2 since I have to classes of images to ...
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What is the most computationally efficient genetic algorithm?
In researching genetic algorithms, it seems that there are various methods of selection and other operator methods that can significantly change the performance. For example, this picture contains ...
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How do I determine the genomes to use for crossover in NEAT?
If I have the fitness of each genome, how do I determine which genome will crossover with which, and so on, so that I get a new population?
Unfortunately, I can't find anything about it in the ...
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What is the impact of changing the crossover and mutation rates?
What is the impact of using a:
low crossover rate
high crossover rate
low mutation rate
high mutation rate
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How does crossover work in a genetic algorithm?
If I had the weights of a certain number of "parents" that I wanted to crossbreed, and I used whatever method to pick out the "best parents" (I used a roulette wheel option, if that's any relevant), ...
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What is meant by "reproduction" in the description of this exercise?
In the following exercise, the word reproduction is mentioned.
Your task is to design a simple genetic algorithm, with binary-coded chromosomes, in order to solve a pattern-finding problem in 16-bit ...
user3642
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Does pairing children with their parents cause any harm (in a genetic program)?
If you pair parents with their children (with a cross-over) does this prevent making individuals which are more fit or does this cause other side effects which are harmful to the genetic process?
I ...
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Do I have to crossover my node genes in NEAT, and how?
I'm currently trying to code the NEAT algorithm by myself, but I got stuck with two questions. Here they are:
What happens if during crossover a node is removed (or disabled) and there's a connection ...
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How can we design the mutation and crossover operations when the order of the genes in the chromosomes matters?
Consider an optimization problem that involves a set of tasks $T = \{1,2,3,4,5\}$, where the goal is to find a certain order of these tasks.
I would like to solve this problem with a genetic algorithm,...
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How to handle infeasibility caused due to crossover and mutation in genetic algorithm for optimization?
I have chromosomes with floating-point representation with values between $0$ and $1$. For example
Let $p_1 = [0.1, 0.2, 0.3]$ and $p_2 = [0.5, 0.6, 0.7]$ be two parents. Both comply with the set of ...
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How to crossover chromosomes composed of genes that are tuples such that the elements of the tuples do not appear twice in the chromosome?
Each chromosome contains an array of genes, each gene contains a letter and a number, both letter and number can only exist once in each chromosome.
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How does the crossover operator work when my output contains only 2 states?
I'm currently working on a project where I am using a basic cellular automata and a genetic algorithm to create dungeon-like maps. Currently, I'm having an incredibly hard time understanding how ...
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Is there a crossover that also considers that every index in the vector also influences the fitness function?
Is there a crossover that also considers that every index in the vector also influences the cost function?
I have two vectors $v_1=[A_1, A_2, A_3, A_4, A_5]$ and $v_2=[A_5, A_3, A_2, A_1, A_4]$.
The ...
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Are there clever (fitness-based) crossover operators for binary chromosomes?
While studying genetic algorithms, I've come across different crossover operations used for binary chromosomes, such as the 1-point crossover, the uniform crossover, etc. These methods usually don't ...
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How should the 1-point crossover and mutation be defined for the problem of finding the largest circle that does not enclose any point?
For a random scattering of points, in a bounded area, the goal is to find the largest circle that can be drawn inside those same bounds that does not enclose any points. Solving this problem with a ...
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Crossover method for gene value containing a set of values
I have a chromosome where each gene contain s set of values. Like the following:
chromosome = [[A,B,C],[C,B,A],[C,D,],[],[E,F]]
The order in each gene values matters. (A,B,C is different to A,C,B)
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How to effectively crossover mathematical curves?
I'm trying to optimize some reflective properties of curves of the form:
$a_1x^n+a_2x^{n-1}+a_3x^{n-2} + ... + a_n + b_1y^n+b_2y^{n-1}+b_3y^{n-2} + ... + b_n = 0$
which is basically the curve that ...
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What does "In each generation, 25% of offspring resulted from mutation without crossover" mean in the context of NEAT?
I am reading through the NEAT paper. In parameter settings, page 15, there is:
In each generation, 25% of offspring resulted from mutation without crossover.
What does it mean?
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